OUTLINE Introduction to Systems Biology Biological Networks

OUTLINE

Introduction to Systems Biology Biological Networks

Introduction to Systems Biology

First introduced in 1934, By Austrian biologist Ludwig von

Bertalanffy, He applied the general system theory to

biology.

Introduction to Systems Biology

To fully understand the functioning of cellular processes, whole cells, organisms, and even organisms:– it is not enough to simply assign functions to

individual genes, proteins, and other cellular organisms,

– we need an integrated way to look at the dynamic networks representing the interactions of components.

Introduction to Systems Biology

What is a System:– dynamics of its components,– interaction of components,– we need modeling to understand the mechanism.

Introduction to Systems Biology

The higher-order properties and functions that arise from the interaction of the parts of a system are called emergent properties.– human brain can thought by the interaction of

brain cells,– a single brain cell is incapable of the property of

thought.

Introduction to Systems Biology

Introduction to Systems Biology

A number of web sites make available information about the interacting proteins in a particular pathway.

Introduction to Systems Biology

the glycolytic patway

Introduction to Systems Biology

The interactions in networks can be represented as DEs:– all the interactions between components in a

model need to be represented mathematically,– differential equations are used for

representation of interactions

Introduction to Systems Biology

Example:

Introduction to Systems Biology

Example:

Introduction to Systems Biology

Another example (Tumor Growth Simulation):

Biological Networks

the glycolytic patway

Biological Networks

E. coli:– a single cell,– amazing technology.

Biological Networks

Gene regulation:– Activators increase gene production

– Repressors decrease gene production

Biological Networks

Gene regulation:– Negative feedback loop:

– Positive feedback loop:

Biological Networks

Nodes are proteins (or genes)

Biological Networks

Nodes are proteins (or genes)

Biological Networks

Network motifs:– Subgraphs: which occur in the real network

significantly more than in a suitable random ensemble of network.

Biological Networks

Network motifs:– 3-node subgraphs:

Biological Networks

Network motifs:– 4-node subgraphs:

Biological Networks

Network motifs:– 5-node subgraphs:

9 364 possible subgraphs

Biological Networks

Network motifs:– 6-node subgraphs:

1 530 843 possible subgraphs

Biological Networks

Find network motifs (ALGORITHM):

Biological Networks

Find network motifs (EXAMPLE):– Network motifs in E. coli

Biological Networks

Find network motifs (EXAMPLE):– Network motifs in E. coli– only one 3-node network motif is significant.

Biological Networks

Network motifs:– Network motifs are functional building blocks of

these information processing networks.– Each motif can be studied theoretically and

experimentally.

Biological Networks

Other networks:– enzyme – lignad

metabolic pathways

– protein – protein cell signaling pathways,

Biological Networks

Pathways:– Pathways are subsets of networks,– Pathways are networks of interactions,– Pathways are related to a known physiological

process or complete function.

Biological Networks

Pathways EXAMPLE:

Biological Networks

Problems:– Source of interaction data is basicly the

experiments,– But in these experiments:

low quality, false positive, false negative.

Biological Networks

Problems SOLUTION:– Probabilistic networks.

Biological Networks

Other Problems:– Network reliability:

What is the probability that some path of functioning wires connects two terminals at a given time?

Biological Networks

Other Problems:– Finding the best simple path (each vertex is

visited once, no cycles) of length k starting from a given node in the graph:

References

M. Zvelebil, J. O. Baum, “Understanding Bioinformatics”, 2008, Garland Science

Andreas D. Baxevanis, B.F. Francis Ouellette, “Bioinformatics: A practical guide to the analysis of genes and proteins”, 2001, Wiley.

Barbara Resch, “Hidden Markov Models - A Tutorial for the Course Computational Intelligence”, 2010.

Wang, Z., Zhang, L., Sagotsky, J., Deisboeck. T. S. (2007), Simulating non-small cell lung cancer with a multiscale agent-based model, Theoretical Biology & Medical Modelling.